Today, we're expanding beyond machine learning competitions and opening Kaggle Datasets up to everyone. You can now instantly share and publish data through Kaggle. This creates a home for your dataset and a place for our community to explore it. Your data immediately becomes available in Kaggle Kernels, meaning that all analysis and insights are shared alongside the dataset.

Machine-learning systems excel at prediction. A common approach is to train a system by showing it a vast quantity of data on, say, students and their achievements. The software chews through the examples and learns which characteristics are most helpful in predicting whether a student will drop out. Once trained, it can study a different group and accurately pick those at risk. By helping to allocate scarce public funds more accurately, machine learning could save governments significant sums. According to Stephen Goldsmith, a professor at Harvard and a former mayor of Indianapolis, it could also transform almost every sector of public policy.

The easiest way to think of their relationship is to visualize them as concentric circles with AI -- the idea that came first -- the largest, then machine learning -- which blossomed later, and finally deep learning -- which is driving today's AI explosion -- fitting inside both.